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- OpenAI ChatGPT - AI assistant for conversation and task completion
- OpenAI Documentation - Official API docs and guides
- Google Gemini - Google's multimodal AI model
- Google AI Studio - Build with Gemini models
- Anthropic Claude - AI assistant focused on helpful, harmless, and honest interactions
- Anthropic Documentation - Claude API and safety resources
- Microsoft 365 Copilot - AI-powered productivity assistant for Microsoft 365
- Microsoft 365 Copilot Documentation - Setup and usage guides
- GitHub Copilot - AI pair programmer for developers
- GitHub Copilot Documentation - Getting started and best practices
- OpenAI Prompt Engineering Guide - Official OpenAI best practices
- OpenAI GPT-4 Technical Report - Technical details and capabilities
- Anthropic Claude Prompt Engineering - Claude-specific prompting techniques
- Anthropic Constitutional AI - Safety-focused AI principles
- Google AI Prompt Best Practices - Google's guidelines for effective prompting
- Google Gemini API Guide - Gemini API documentation and examples
- Microsoft AI Builder Prompts - Microsoft's prompt design patterns
- Microsoft Semantic Kernel - AI orchestration framework
- AWS Prompt Engineering Guide - Amazon's approach to prompt engineering
- AWS Bedrock Prompt Engineering - Bedrock-specific prompting
- Meta Llama Prompt Engineering - Llama model prompting guide
- Cohere Prompt Engineering - Cohere's prompting best practices
- Hugging Face Prompt Engineering - Transformers library guidance
- Perplexity - AI-powered search and discovery
- Cursor - AI-enhanced development environment
- Kagi Universal Summarizer - Advanced text summarization tool
- Midjourney - AI image generation and art creation
- SlidesGPT - AI-powered presentation generation
- ElevenLabs - AI voice synthesis and text-to-speech
- Heygen - AI video generation and avatar creation
- Poppy AI - AI-powered content creation and automation
- Gamma AI - AI presentation and document creation
- Cline AI - AI-powered code generation and assistance
- Windsurf AI - AI-powered data analysis and visualization
- Sora - OpenAI's text-to-video generation model
- Operator - AI-powered business automation platform
- OpenAI Cookbook - Practical prompt engineering recipes
- Anthropic Claude Documentation - Advanced prompting techniques
- Prompt Engineering Guide - Comprehensive learning resource
- Microsoft Learn AI Skills Challenge - Microsoft AI training
- Google AI Learning Path - Google AI education
- AWS Machine Learning University - AWS ML training
- Azure OpenAI Service - Enterprise OpenAI models on Azure
- Copilot Studio - Build custom copilots and AI agents
- Azure AI Services - Complete AI service portfolio
- M365 Copilot Development - Extend Microsoft 365 Copilot
- Hugging Face - Open-source model hub and community
- Ollama - Local LLM deployment and management
- Kagi - AI-powered search engine
- You.com - AI search and chat platform
- GitHub Models - LLM repositories on GitHub
- GitHub Copilot Extensions - Copilot marketplace extensions
- Kagi Universal Summarizer - Advanced text summarization
- Perplexity Labs - AI research and experimentation
- Claude Search - Claude-powered search
- GitHub Copilot Documentation - Copilot development guide
- Cursor Documentation - Cursor IDE documentation
- Amazon CodeWhisperer - AWS code generation
- Microsoft Responsible AI Standards
- OpenAI Safety & Responsibility
- Google AI Principles
- AWS Responsible AI
- Anthropic AI Safety
- Meta AI Safety - Meta's responsible AI approach
- IBM AI Ethics - IBM's AI ethics framework
- UNESCO AI Ethics - Global AI ethics framework
- OECD AI Principles - International AI policy principles
- MCP Official Specification - Official MCP documentation and spec
- MCP GitHub Repository - Official MCP GitHub organization
- MCP Server Gallery - Collection of MCP servers and tools
- MCP Python SDK - Python implementation
- MCP TypeScript SDK - TypeScript implementation
- MCP Rust SDK - Rust implementation
- MCP Server Examples - Example MCP server implementations
- Maintain at least 2 "daily driver" LLMs at a paid tier for A/B testing (fault tolerance and groundedness)
- Never provide personal or confidential information to public/free AIs—ensure privacy by understanding chat storage, usage stats, and licensing policies
- Speak to the LLM in ways most comfortable to you (voice, text, image) and take advantage of its multi-modal capabilities
- Apply a stream-of-consciousness technique to generate prompts, even with rough spelling/grammar, including key information like who, what, when, where, why, and how
- Think procedurally and in a step-by-step manner to help the AI break down complex topics
- Optimize custom instructions and prompts ("meta prompting"), including asking the AI to summarize or focus its responses
- Use system prompts and meta prompts to direct and focus the LLM's capabilities
- Be aware of potential signs of amnesia or hallucination in AI responses; have a backup plan (such as testing with multiple LLMs)
- Accept that you'll never be fully caught up—embrace exploration, questioning, and constant testing
- Build cognitive "muscle memory" with AI by practicing prompt refinement and cross-model comparisons
- Remember to attribute AI-enriched content where relevant
- Understand the unique strengths and behaviors of each LLM and leverage them strategically in multi-chat sessions
- "LLM Pillar Jumping": Use insights from one LLM session to support or refine another
- Consider "A/B testing" LLMs against each other for more grounded and reliable answers
- Get vulnerable with your AI (in trusted, secure sessions) to receive maximally personalized results—the more context you provide about your unique situation, the more tailored and valuable the response
- Leverage "meta-prompting" by asking the AI to craft system messages, design prompts, and optimize instructions—let the AI help you become better at using AI